Abstract

The healthy human nervous system accurately and robustly controls movements despite nonlinear dynamics, noise, and delays. After a stroke, motor ability frequently becomes impaired. To provide insight into the relative impact of specific sensorimotor deficits on motor performance, we modeled neural control of reaching with the human upper limb as a near-optimally feedback-controlled two-degree-of-freedom system with biologically based parameters. We added three sensorimotor impairments commonly associated with post-stroke hemiparesis - abnormal joint coupling, increased noise on internally modeled dynamics, and muscular weakness - and examined the impact on reaching performance. We found that abnormal joint coupling unknown to the system's internal model caused systematic perturbations to trajectories, longer reach durations, and target overshoot. Increasing internal model noise and muscular weakness had little impact on motor performance unless model noise was increased by several orders of magnitude. Many reaches performed by our perturbed models replicate features commonly observed in reaches by hemiparetic stroke survivors. The sensitivity to unmodeled abnormal joint coupling agrees with experimental findings that abnormal coupling (possibly related to internal model errors) is the main cause of post-stroke motor impairment.